Developing and Implementing Peer-Led Intervention to Support Staff in Long-Term Care Homes Manage Grief
Bibliographic record
Abstract
Front-line staff in long-term care (LTC) homes often form strong emotional bonds with residents. When residents die, staffs’ grief often goes unattended, and may result in disenfranchised grief. The aim of this article is to develop, implement, and assess the benefits of a peer-led debriefing intervention to help staff manage their grief and provide LTC homes an organizational approach to support them. This research was nested within a 5-year participatory action research to develop and implement palliative care programs within four LTC homes in Canada. Data specific to this debriefing intervention included questionnaires from six peer debriefers, field observations of six debriefings, and qualitative interviews with 23 staff participants. An original peer-led debriefing intervention (INNPUT) for LTC home staff was developed and implemented. Data revealed that the intervention offered staff an opportunity to express grief in a safe context with others, an opportunity for closure and acknowledgment. The INNPUT intervention benefits staff and offers an innovative, sustainable, easy to use strategy for LTC homes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".